Nuclear Mass Predictions of the Relativistic Density Functional Theory with the Kernel Ridge Regression and the Application to r-Process Simulations

Symmetry

Lihan Guo¹  Xinhui Wu¹  Pengwei Zhao¹ 

[1] State Key Laboratory of Nuclear Physics and Technology, School of Physics, Peking University, Beijing 100871, China;

关键词: nuclear mass;    machine learning;    kernel ridge regression;    relativistic density functional theory;    r-process;   

DOI  :  10.3390/sym14061078

来源: DOAJ

【 摘 要 】

The kernel ridge regression (KRR) and its updated version taking into account the odd-even effects (KRRoe) are employed to improve the mass predictions of the relativistic density functional theory. Both the KRR and KRRoe approaches can improve the mass predictions to a large extent. In particular, the KRRoe approach can significantly improve the predictions of the one-nucleon separation energies. The extrapolation performances of the KRR and KRRoe approaches to neutron-rich nuclei are examined, and the impacts of the KRRoe mass corrections on the r-process simulations are studied. It is found that the KRRoe mass corrections for the nuclei in the r-process path are remarkable in the light mass region, e.g., $A<150$, and this could influence the corresponding r-process abundances.

【 授权许可】

Unknown